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c91d7b1 64f5718 c91d7b1 64f5718 c91d7b1 64f5718 c91d7b1 64f5718 c91d7b1 64f5718 c91d7b1 64f5718 c91d7b1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | import torch
import numpy as np
import torch.nn.functional as F
from deeprobust.graph.defense import RGCN
from deeprobust.graph.utils import *
from deeprobust.graph.data import Dataset, PrePtbDataset
import argparse
import os
import csv
parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=15, help='Random seed.')
parser.add_argument('--dataset', type=str, default='Flickr', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed', 'Flickr'], help='dataset')
parser.add_argument('--ptb_rate', type=float, default=0.25, help='pertubation rate')
parser.add_argument('--ptb_type', type=str, default='minmax', choices=['clean', 'meta', 'dice', 'minmax', 'pgd', 'random'], help='attack type')
parser.add_argument('--hidden', type=int, default=16, help='Number of hidden units.')
parser.add_argument('--dropout', type=float, default=0.5, help='Dropout rate (1 - keep probability).')
parser.add_argument('--gpu', type=int, default=0, help='GPU device ID (default: 0)')
args = parser.parse_args()
args.cuda = torch.cuda.is_available()
print('cuda: %s' % args.cuda)
device = torch.device(f"cuda:{args.gpu}" if torch.cuda.is_available() else "cpu")
# make sure you use the same data splits as you generated attacks
np.random.seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
# Here the random seed is to split the train/val/test data,
# we need to set the random seed to be the same as that when you generate the perturbed graph
# data = Dataset(root='/tmp/', name=args.dataset, setting='nettack', seed=15)
# Or we can just use setting='prognn' to get the splits
# data = Dataset(root='/tmp/', name=args.dataset, setting='prognn')
data = Dataset(root='/tmp/', name=args.dataset)
adj, features, labels = data.adj, data.features, data.labels
idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
# adj, features, labels = preprocess(adj, features, labels, preprocess_adj=False)
## load from attacked_adj
ptb_path = f"../attacked_adj/{args.dataset}/{args.ptb_type}_{args.dataset}_{args.ptb_rate}.pt"
perturbed_adj = torch.load(ptb_path)
perturbed_adj = perturbed_adj
def test_rgcn(adj):
''' test on GCN '''
# adj = normalize_adj_tensor(adj)
gcn = RGCN(nnodes=adj.shape[0], nfeat=features.shape[1], nclass=labels.max()+1,
nhid=args.hidden, device=device)
gcn = gcn.to(device)
gcn.fit(features, adj, labels, idx_train, idx_val, train_iters=200, verbose=True)
gcn.eval()
acc = gcn.test(idx_test)
return acc
def main():
acc = test_rgcn(perturbed_adj)
csv_dir = "../result"
os.makedirs(csv_dir, exist_ok=True)
csv_filename = os.path.join(csv_dir, f"RGCN_{args.dataset}_{args.ptb_type}_{args.ptb_rate}.csv")
row = [f"{args.dataset} ", f" {args.ptb_type} ", f" {args.ptb_rate} ", f" {acc}"]
try:
file_exists = os.path.isfile(csv_filename)
with open(csv_filename, 'a', newline='') as csvfile:
writer = csv.writer(csvfile)
if not file_exists:
writer.writerow(["dataset ", "ptb_type ", "ptb_rate ", "accuracy"])
writer.writerow(row)
except Exception as e:
print(f"[Error] Failed to write CSV: {e}")
# # if you want to save the modified adj/features, uncomment the code below
# model.save_adj(root='./', name=f'mod_adj')
# model.save_features(root='./', name='mod_features')
if __name__ == '__main__':
main() |